code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--msmarco_train_input_filepath",
          default=None,
          type=str,
          required=True,
      )
      parser.add_argument(
          "--msmarco_dev_input_filepath",
          default=None,
          type=str,
          required=True,
      )
      parser.add_argument(
          "--converted_train_save_path",
          default=None,
          type=str,
          required=True,
      )
      parser.add_argument(
          "--converted_dev_save_path",
          default=None,
          type=str,
          required=True,
      )
      parser.add_argument(
          "--exclude_negative_samples",
          default=False,
          type=bool,
          help="whether to keep No Answer samples in the dataset",
          required=False,
      )
      parser.add_argument(
          "--keep_only_relevant_passages",
          default=False,
          type=bool,
          help="if True, will only use passages with is_selected=True for context",
          required=False,
      )
      args = parser.parse_args()
  
      print("converting MS-MARCO train dataset...")
      msmarco_train_data = load_json(args.msmarco_train_input_filepath)
      squad_train_data = convert_msmarco_to_squad_format(
          msmarco_train_data, args
      )
      dump_json(args.converted_train_save_path, squad_train_data)
  
      print("converting MS-MARCO dev dataset...")
      msmarco_dev_data = load_json(args.msmarco_dev_input_filepath)
      squad_dev_data = convert_msmarco_to_squad_format(
          msmarco_dev_data, args
      )
      dump_json(args.converted_dev_save_path, squad_dev_data)
  
file_path: NeMo/examples/nlp/question_answering/convert_msmarco_to_squad_format.py
imports: import argparse
  import json
  from ast import literal_eval
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types